Command R vs Llama 4 Scout
Compare Cohere and Meta (via Together AI) AI models
Cost Comparison (1000 input + 500 output tokens, 100 requests/day)
Command R
Llama 4 Scout
Cost Differences
Llama 4 Scout costs less than Command R
Feature Comparison
| Feature | Command R | Llama 4 Scout |
|---|---|---|
| Provider | Cohere | Meta (via Together AI) |
| Input Price | $0.15/1M tokens | $0.08/1M tokens |
| Output Price | $0.60/1M tokens | $0.30/1M tokens |
| Context Window | 128,000 tokens | 10,000,000 tokens |
| Max Output | 4,096 tokens | 32,768 tokens |
| Category | efficient | efficient |
| Capabilities | textcode | textvisioncode |
| Release Date | 3/11/2024 | 4/5/2025 |
Command R vs Llama 4 Scout: Which Should You Choose?
Choosing between Command R and Llama 4 Scout depends on your priorities: cost efficiency, context length, or raw capability. Llama 4 Scout is the more affordable option at $0.08/1M input tokens — 47% cheaper than Command R. Meanwhile, Llama 4 Scout offers a significantly larger context window at 10,000,000 tokens vs 128,000 for Command R.
These models come from different providers — Cohere and Meta (via Together AI) — which means different API ecosystems, SDKs, rate limits, and terms of service. If you're already integrated with Cohere, switching to Meta (via Together AI)involves migration effort beyond just pricing. Factor in your existing infrastructure when deciding.
Both models are in the efficient category, making this a direct head-to-head comparison. At scale — say 10,000 requests per day — the cost difference adds up: Llama 4 Scout would save you roughly $66.00/month compared to Command R. For startups and indie developers, that difference can be significant.
Output costs matter too. Command R charges $0.60/1M output tokens vs $0.30 for Llama 4 Scout. For generation-heavy workloads (content creation, code generation, summarization), output pricing often dominates your bill. Llama 4 Scout has the edge here at $0.30/1M output tokens.
Multimodal capabilities: Llama 4 Scout supports vision (image inputs) while Command R is text-only. If your application needs image understanding, this narrows your choice.
Best Use Cases
Choose Command R when:
- • You're already using Cohere's API ecosystem
- • You're running high-volume, latency-sensitive workloads
Choose Llama 4 Scout when:
- • Budget is a primary concern
- • You need a larger context window (10,000,000 tokens)
- • You need more capabilities (vision)
- • You need longer outputs (up to 32,768 tokens)
- • You're already using Meta (via Together AI)'s API ecosystem
- • You're running high-volume, latency-sensitive workloads
Try Different Scenarios
Use the calculator below to see how costs change with different usage patterns
Command R (Cohere)
Llama 4 Scout (Meta (via Together AI))
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